6 results about "Unified Model" patented technology

The invention discloses an electric power information physical system risk identification method and system in extreme weather, and the method comprises the steps: introducing the influence of extreme weather into an information-energy flow model of an electric power information physical system, and building an information-energy-extreme weather unified model; n-2 fault scanning is carried out on a power-communication coupling line of the power information physical system to obtain a fault scene set, risks of the power information physical system in all fault scenes in the fault scene set are analyzed and recognized based on an information-energy-extreme weather unified model, and a risk recognition result is obtained; therefore, the influence of extreme weather is considered in the information-energy flow model, the information-physical interaction process can be accurately described, traditional line / element N-1 fault scanning is expanded into power-communication coupling line N-2 fault scanning, a more robust and practical fault scene set can be obtained, the risk identification result is more accurate, the calculation speed is high, and the method is suitable for popularization and application. Therefore, the accuracy of risk identification is improved, and the lower complexity is ensured.

The invention belongs to the technical field of photovoltaic power prediction, and particularly relates to a photovoltaic power multi-time scale prediction method based on machine learning, which comprises the following steps: acquiring original photovoltaic power time sequence data and weather forecast data, and preprocessing; a multi-input multi-output modeling strategy is adopted, a differential modeling strategy is adopted for different time scales, a multi-scale power prediction model is constructed, and the multi-scale power prediction model is trained based on the processed data; and performing prediction output based on the trained multi-scale power prediction model, and evaluating the trained power prediction model for different weather data in different regions. Through a unified model architecture and a collaborative optimization mechanism, the computing resource consumption and the operation and maintenance cost are reduced, an effective error feedback and collaborative optimization mechanism is formed, and the overall prediction efficiency is remarkably improved.

The invention discloses a photovoltaic snow covering depth and power loss forecasting method and system based on multi-source data fusion, and belongs to the technical field of photovoltaic power forecasting. The problems of high-precision accumulated snow influence assessment, short-term risk early warning and power loss quantitative prediction of a photovoltaic system in cold and alpine regions are solved. The method comprises the following steps: selecting three numerical weather forecasting modes, namely a global forecasting system operated by the National Environmental Forecasting Center, a wind energy and solar energy forecasting system of the China Meteorological Administration and a unified mode weather forecast of the British Meteorological Administration, acquiring weather forecasting data, performing unified space-time interpolation, abnormal value correction and unit conversion, and then performing set averaging to obtain a set average value; ensemble forecast data is obtained; based on a temperature-irradiance condition and a friction force mechanism, an accumulated snow covering model is constructed, a photovoltaic panel snow covering range and photovoltaic power loss are obtained, early warning information of snow covering time, duration and power loss is generated through matching of station longitude and latitude and national grid forecasting, and photovoltaic snow covering depth and power loss forecasting based on multi-source data fusion is completed.

This application discloses an artificial intelligence-based wind power prediction data processing method, relating to the field of new energy power system operation and control technology. The method includes: acquiring the predicted wind power of a target wind farm at the current moment; determining the cluster type label; loading a classification reinforcement learning correction model corresponding to the cluster type; calculating correction coefficients through the classification reinforcement learning correction model; and correcting the predicted wind power based on the correction coefficients to obtain the corrected wind power. This application, by introducing cluster type labels and loading corresponding classification reinforcement learning correction models, adaptively corrects wind power prediction based on the characteristics of different wind farm clusters. This effectively solves the problem that traditional unified models cannot cope with extreme weather and special operating conditions, avoiding correction failure or reverse correction caused by misjudging large deviations as noise, and significantly improving the accuracy and robustness of wind power prediction.

The invention relates to the technical field of arid region SPEI index prediction, in particular to an arid region SPEI index prediction method and system based on a time sequence prediction model. A target area is gridded, and a prediction model is independently established for each grid point, so that the problem of drought signal space averaging caused by parameter sharing of a traditional global unified model is solved, and local climate characteristics and drought evolution rules can be caught and reflected more finely; therefore, the prediction precision and reliability of the SPEI index in the spatial distribution of the arid region are remarkably improved, trend residual decomposition is performed on the SPEI data of each grid point by adopting an XGBoost regression model, and a trend component is extracted as an auxiliary sequence to be input into a prediction model, so that the non-stationary influence of a time sequence is effectively reduced, the adaptability of the model to long-term climate change is enhanced, and the prediction accuracy and reliability of the SPEI index in the arid region are improved. Meanwhile, overfitting is restrained through regularization and conservative parameter setting, and the stability and generalization performance of the model in long-time-sequence forecasting are improved.

The present application belongs to the technical field of photovoltaic power prediction, and particularly relates to a photovoltaic power multi-time scale prediction method based on machine learning, comprising: obtaining original photovoltaic power time series data and weather forecast data, and performing preprocessing; adopting a multi-input multi-output modeling strategy, adopting a differentiated modeling strategy for different time scales, constructing a multi-scale power prediction model, and training the multi-scale power prediction model based on the processed data; performing prediction output based on the trained multi-scale power prediction model, and evaluating the trained power prediction model for different weather data in different regions. Through a unified model architecture and a collaborative optimization mechanism, the calculation resource consumption and operation and maintenance cost are reduced, an effective error feedback and collaborative optimization mechanism is formed, and the overall prediction efficiency is significantly improved.